Advancing the understanding of exoplanets and planet formation requires a wide variety of observational methods and data modalities. Planet formation is a complex process that involves the assembly of a planet from a protoplanetary disk, an environment that instruments have only recently been able to resolve. These observations rely mostly on image data, including line emission and continuum data. The analysis of this data is a complex process, but, when done successfully, it opens new avenues for understanding planet formation, the resulting systems of exoplanets, and the potential of these systems for habitability. A complementary route is to use data from the atmospheres of exoplanets. The characterization of exoplanet atmospheres is crucial for understanding their compositions, weather patterns, and potential habitability. This project aims to develop a foundation machine learning models that will analyze data of different environments from different instruments to further our understanding of planet formation, extoplanet systems, exoplanet properties, and, ultimately, the potential of these systems for habitability. The models will use image data of disks, spectral data from exoplanets, identifying forming exoplanets, processes and substructures that are important in protoplanetary disk evolution, chemical abundances in exoplanet atmosphers, cloud/haze structure, and different atmospheric processes. The project will leverage data from telescopes and space missions, along with simulations of protoplanetary disks and exoplanetary atmospheres under various conditions, to train and validate the models.
Total project length: 175/350 hours.
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